Secure Safety Filter: Towards Safe Flight Control under Sensor Attacks

TL;DR

This paper introduces a secure safety filter for drone flight control that detects and mitigates sensor attacks, ensuring safety despite noise and nonlinear dynamics, validated through simulations and hardware tests.

Contribution

It extends control barrier functions to handle sensor attacks in nonlinear drone systems using a modular approach with state reconstruction.

Findings

Effective in mitigating sensor attacks in drone control

Handles bounded measurement noise and nonlinear dynamics

Validated through software and hardware experiments

Abstract

Modern autopilot systems are prone to sensor attacks that can jeopardize flight safety. To mitigate this risk, we proposed a modular solution: the secure safety filter, which extends the well-established control barrier function (CBF)-based safety filter to account for, and mitigate, sensor attacks. This module consists of a secure state reconstructor (which generates plausible states) and a safety filter (which computes the safe control input that is closest to the nominal one). Differing from existing work focusing on linear, noise-free systems, the proposed secure safety filter handles bounded measurement noise and, by leveraging reduced-order model techniques, is applicable to the nonlinear dynamics of drones. Software-in-the-loop simulations and drone hardware experiments demonstrate the effectiveness of the secure safety filter in rendering the system safe in the presence of sensor attacks.